A point cloud feature-based obstacle filter system

ABSTRACT

A method, apparatus, and system for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. One or more obstacle candidates are determined based on the point cloud. The one or more obstacle candidates are filtered to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates. One or more recognized obstacles comprising the obstacle candidates that have not been removed are determined. Operations of an autonomous vehicle are controlled based on the recognized obstacles.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operating autonomous vehicles. More particularly, embodiments of the disclosure relate to processing obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.

LIDAR (Light Detection and Ranging, or a portmanteau of light and radar) techniques have been widely utilized in military, geography, oceanography, and in the latest decade, autonomous driving vehicles. A LIDAR device can estimate a distance to an object while scanning through a scene to assemble a point cloud representing a reflective surface of the object. Individual points in the point cloud can be determined by transmitting a laser pulse and detecting a returning pulse, if any, reflected from the object, and determining the distance to the object according to the time delay between the transmitted pulse and the reception of the reflected pulse. A laser or lasers, can be rapidly and repeatedly scanned across a scene to provide continuous real-time information on distances to reflective objects in the scene. Each full rotation of one laser beam produces one ring of points.

The LIDAR technique is susceptible to noise, such as that caused by dusts or other particles. A noisy point cloud may cause the perception module to generate obstacles that are false positives.

SUMMARY

Embodiments of the present disclosure provide a computer-implemented method, a non-transitory machine-readable medium, and a data processing system.

Some embodiments of the present disclosure provide a computer-implemented method, the method includes: generating a point cloud comprising a plurality of points based on outputs of a light detection and range (LIDAR) device; determining one or more obstacle candidates based on the point cloud; filtering the one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates; determining one or more recognized obstacles comprising the obstacle candidates that have not been removed; and controlling operations of an autonomous vehicle based on the recognized obstacles.

Some embodiments of the present disclosure provide a non-transitory machine-readable medium, the medium has instructions stored therein, which when executed by a processor, cause the processor to perform operations, the operations includes: generating a point cloud comprising a plurality of points based on outputs of a light detection and range (LIDAR) device; determining one or more obstacle candidates based on the point cloud; filtering the one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates; determining one or more recognized obstacles comprising the obstacle candidates that have not been removed; and controlling operations of an autonomous vehicle based on the recognized obstacles.

Some embodiments of the present disclosure provide a data processing system, the system includes: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations, the operations including: generating a point cloud comprising a plurality of points based on outputs of a light detection and range (LIDAR) device; determining one or more obstacle candidates based on the point cloud; filtering the one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates; determining one or more recognized obstacles comprising the obstacle candidates that have not been removed; and controlling operations of an autonomous vehicle based on the recognized obstacles.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according to one embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomous vehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perception and planning system used with an autonomous vehicle according to one embodiment.

FIG. 4 is a block diagram illustrating various modules usable according to one embodiment of the disclosure.

FIGS. 5A and 5B are diagrams illustrating a spatial distribution of LIDAR points that correspond to an obstacle according to one embodiment.

FIG. 6 is a flowchart illustrating an example method for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle according to one embodiment.

FIG. 7 is a flowchart illustrating an example method for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle according to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.

According to some embodiments, a method, apparatus, and system for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. One or more obstacle candidates are determined based on the point cloud. The one or more obstacle candidates are filtered to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates. One or more recognized obstacles comprising the obstacle candidates that have not been removed are determined. Operations of an autonomous vehicle are controlled based on the recognized obstacles.

In one embodiment, the first set of obstacle candidates that correspond to noise comprise obstacle candidates that correspond to points corresponding to dusts. In one embodiment, the characteristics associated with points that correspond to each of the one or more obstacle candidates based on which the one or more obstacle candidates are filtered comprise one or more of: an intensity measurement distribution of the points that correspond to each of the one or more obstacle candidates, a spatial distribution of the points that correspond to each of the one or more obstacle candidates, or a combination thereof.

In one embodiment, filtering the one or more obstacle candidates comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low intensity measurements to a total quantity of points corresponding to the obstacle candidate is above a first threshold. A point is associated with a low intensity measurement when the intensity measurement of the point is below an intensity threshold.

In one embodiment, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold, points corresponding to the obstacle candidate are projected to a first and a second dimensions of a horizontal plane. The first dimension and the second dimension are orthogonal to each other. A first standard deviation of the projected points along the first dimension and a second standard deviation of the projected points along the second dimension are determined. The obstacle candidate is removed when the first standard deviation is above a first standard deviation threshold, or when the second standard deviation is above a second standard deviation threshold. In one embodiment, the obstacle candidate is removed when the first standard deviation is above the first standard deviation threshold and the second standard deviation is above the second standard deviation threshold.

In one embodiment, the first standard deviation threshold is equal to the second standard deviation threshold. In another embodiment, the first standard deviation threshold and the second standard deviation threshold are different.

In one embodiment, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold, points corresponding to the obstacle candidate are projected to an area of the horizontal plane. The area of the horizontal plane is associated with a grid comprising a first quantity of squares. Within the first quantity of squares, a second quantity of squares each of which contains at least one projected point are determined. The obstacle candidate is removed when a ratio of the second quantity to the first quantity is above a second threshold.

In one embodiment, filtering the one or more obstacle candidates further comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low heights to a total quantity of points corresponding to the obstacle candidate is below a third threshold. A point is associated with a low height when the height of the point is below a height threshold.

In one embodiment, filtering the one or more obstacle candidates further comprises removing obstacle candidates each of which is absent in perception results for any of a predetermined number of immediately previous perception cycles.

FIG. 1 is a block diagram illustrating an autonomous vehicle network configuration according to one embodiment of the disclosure. Referring to FIG. 1, network configuration 100 includes autonomous vehicle 101 that may be communicatively coupled to one or more servers 103-104 over a network 102. Although there is one autonomous vehicle shown, multiple autonomous vehicles can be coupled to each other and/or coupled to servers 103-104 over network 102. Network 102 may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof, wired or wireless. Server(s) 103-104 may be any kind of servers or a cluster of servers, such as Web or cloud servers, application servers, backend servers, or a combination thereof. Servers 103-104 may be data analytics servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to in an autonomous mode in which the vehicle navigates through an environment with little or no input from a driver. Such an autonomous vehicle can include a sensor system having one or more sensors that are configured to detect information about the environment in which the vehicle operates. The vehicle and its associated controller(s) use the detected information to navigate through the environment. Autonomous vehicle 101 can operate in a manual mode, a full autonomous mode, or a partial autonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limited to, perception and planning system 110, vehicle control system 111, wireless communication system 112, user interface system 113, and sensor system 115. Autonomous vehicle 101 may further include certain common components included in ordinary vehicles, such as, an engine, wheels, steering wheel, transmission, etc., which may be controlled by vehicle control system 111 and/or perception and planning system 110 using a variety of communication signals and/or commands, such as, for example, acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via an interconnect, a bus, a network, or a combination thereof. For example, components 110-115 may be communicatively coupled to each other via a controller area network (CAN) bus. A CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer. It is a message-based protocol, designed originally for multiplex electrical wiring within automobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes, but it is not limited to, one or more cameras 211, global positioning system (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit 214, and a light detection and range (LIDAR) unit 215. GPS system 212 may include a transceiver operable to provide information regarding the position of the autonomous vehicle. IMU unit 213 may sense position and orientation changes of the autonomous vehicle based on inertial acceleration. Radar unit 214 may represent a system that utilizes radio signals to sense objects within the local environment of the autonomous vehicle. In some embodiments, in addition to sensing objects, radar unit 214 may additionally sense the speed and/or heading of the objects. LIDAR unit 215 may sense objects in the environment in which the autonomous vehicle is located using lasers. LIDAR unit 215 could include one or more laser sources, a laser scanner, and one or more detectors, among other system components. Cameras 211 may include one or more devices to capture images of the environment surrounding the autonomous vehicle. Cameras 211 may be still cameras and/or video cameras. A camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a braking sensor, and an audio sensor (e.g., microphone). An audio sensor may be configured to capture sound from the environment surrounding the autonomous vehicle. A steering sensor may be configured to sense the steering angle of a steering wheel, wheels of the vehicle, or a combination thereof. A throttle sensor and a braking sensor sense the throttle position and braking position of the vehicle, respectively. In some situations, a throttle sensor and a braking sensor may be integrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is not limited to, steering unit 201, throttle unit 202 (also referred to as an acceleration unit), and braking unit 203. Steering unit 201 is to adjust the direction or heading of the vehicle. Throttle unit 202 is to control the speed of the motor or engine that in turn controls the speed and acceleration of the vehicle. Braking unit 203 is to decelerate the vehicle by providing friction to slow the wheels or tires of the vehicle. Note that the components as shown in FIG. 2 may be implemented in hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allow communication between autonomous vehicle 101 and external systems, such as devices, sensors, other vehicles, etc. For example, wireless communication system 112 can wirelessly communicate with one or more devices directly or via a communication network, such as servers 103-104 over network 102. Wireless communication system 112 can use any cellular communication network or a wireless local area network (WLAN), e.g., using WiFi to communicate with another component or system. Wireless communication system 112 could communicate directly with a device (e.g., a mobile device of a passenger, a display device, a speaker within vehicle 101), for example, using an infrared link, Bluetooth, etc. User interface system 113 may be part of peripheral devices implemented within vehicle 101 including, for example, a keyboard, a touch screen display device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlled or managed by perception and planning system 110, especially when operating in an autonomous driving mode. Perception and planning system 110 includes the necessary hardware (e.g., processor(s), memory, storage) and software (e.g., operating system, planning and routing programs) to receive information from sensor system 115, control system 111, wireless communication system 112, and/or user interface system 113, process the received information, plan a route or path from a starting point to a destination point, and then drive vehicle 101 based on the planning and control information. Alternatively, perception and planning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and a destination of a trip, for example, via a user interface. Perception and planning system 110 obtains the trip related data. For example, perception and planning system 110 may obtain location and route information from an MPOI server, which may be a part of servers 103-104. The location server provides location services and the MPOI server provides map services and the POIs of certain locations. Alternatively, such location and MPOI information may be cached locally in a persistent storage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception and planning system 110 may also obtain real-time traffic information from a traffic information system or server (TIS). Note that servers 103-104 may be operated by a third party entity. Alternatively, the functionalities of servers 103-104 may be integrated with perception and planning system 110. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system 115 (e.g., obstacles, objects, nearby vehicles), perception and planning system 110 can plan an optimal route and drive vehicle 101, for example, via control system 111, according to the planned route to reach the specified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analytics services for a variety of clients. In one embodiment, data analytics system 103 includes data collector 121 and machine learning engine 122. Data collector 121 collects driving statistics 123 from a variety of vehicles, either autonomous vehicles or regular vehicles driven by human drivers. Driving statistics 123 include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics 123 may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for a variety of purposes. In one embodiment, algorithms 124 may include an algorithm for filtering obstacle candidates determined based on outputs of the LIDAR device. Algorithms 124 can then be uploaded on ADVs to be utilized during autonomous driving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of a perception and planning system used with an autonomous vehicle according to one embodiment. System 300 may be implemented as a part of autonomous vehicle 101 of FIG. 1 including, but is not limited to, perception and planning system 110, control system 111, and sensor system 115. Referring to FIGS. 3A-3B, perception and planning system 110 includes, but is not limited to, localization module 301, perception module 302, prediction module 303, decision module 304, planning module 305, control module 306, routing module 307, obstacle filtering module 403.

Some or all of modules 301-307, 403 may be implemented in software, hardware, or a combination thereof. For example, these modules may be installed in persistent storage device 352, loaded into memory 351, and executed by one or more processors (not shown). Note that some or all of these modules may be communicatively coupled to or integrated with some or all modules of vehicle control system 111 of FIG. 2. Some of modules 301-307, 403 may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomous vehicle 300 (e.g., leveraging GPS unit 212) and manages any data related to a trip or route of a user. Localization module 301 (also referred to as a map and route module) manages any data related to a trip or route of a user. A user may log in and specify a starting location and a destination of a trip, for example, via a user interface. Localization module 301 communicates with other components of autonomous vehicle 300, such as map and route information 311, to obtain the trip related data. For example, localization module 301 may obtain location and route information from a location server and a map and POI (MPOI) server. A location server provides location services and an MPOI server provides map services and the POIs of certain locations, which may be cached as part of map and route information 311. While autonomous vehicle 300 is moving along the route, localization module 301 may also obtain real-time traffic information from a traffic information system or server.

Based on the sensor data provided by sensor system 115 and localization information obtained by localization module 301, a perception of the surrounding environment is determined by perception module 302. The perception information may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving. The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system or functionalities of a computer vision system to process and analyze images captured by one or more cameras in order to identify objects and/or features in the environment of autonomous vehicle. The objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc. The computer vision system may use an object recognition algorithm, video tracking, and other computer vision techniques. In some embodiments, the computer vision system can map an environment, track objects, and estimate the speed of objects, etc. Perception module 302 can also detect objects based on other sensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the object will behave under the circumstances. The prediction is performed based on the perception data perceiving the driving environment at the point in time in view of a set of map/rout information 311 and traffic rules 312. For example, if the object is a vehicle at an opposing direction and the current driving environment includes an intersection, prediction module 303 will predict whether the vehicle will likely move straight forward or make a turn. If the perception data indicates that the intersection has no traffic light, prediction module 303 may predict that the vehicle may have to fully stop prior to enter the intersection. If the perception data indicates that the vehicle is currently at a left-turn only lane or a right-turn only lane, prediction module 303 may predict that the vehicle will more likely make a left turn or right turn respectively.

For each of the objects, decision module 304 makes a decision regarding how to handle the object. For example, for a particular object (e.g., another vehicle in a crossing route) as well as its metadata describing the object (e.g., a speed, direction, turning angle), decision module 304 decides how to encounter the object (e.g., overtake, yield, stop, pass). Decision module 304 may make such decisions according to a set of rules such as traffic rules or driving rules 312, which may be stored in persistent storage device 352.

Routing module 307 is configured to provide one or more routes or paths from a starting point to a destination point. For a given trip from a start location to a destination location, for example, received from a user, routing module 307 obtains route and map information 311 and determines all possible routes or paths from the starting location to reach the destination location. Routing module 307 may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location. A reference line refers to an ideal route or path without any interference from others such as other vehicles, obstacles, or traffic condition. That is, if there is no other vehicle, pedestrians, or obstacles on the road, an ADV should exactly or closely follows the reference line. The topographic maps are then provided to decision module 304 and/or planning module 305. Decision module 304 and/or planning module 305 examine all of the possible routes to select and modify one of the most optimal routes in view of other data provided by other modules such as traffic conditions from localization module 301, driving environment perceived by perception module 302, and traffic condition predicted by prediction module 303. The actual path or route for controlling the ADV may be close to or different from the reference line provided by routing module 307 dependent upon the specific driving environment at the point in time.

Based on a decision for each of the objects perceived, planning module 305 plans a path or route for the autonomous vehicle, as well as driving parameters (e.g., distance, speed, and/or turning angle), using a reference line provided by routing module 307 as a basis. That is, for a given object, decision module 304 decides what to do with the object, while planning module 305 determines how to do it. For example, for a given object, decision module 304 may decide to pass the object, while planning module 305 may determine whether to pass on the left side or right side of the object Planning and control data is generated by planning module 305 including information describing how vehicle 300 would move in a next moving cycle (e.g., next route/path segment). For example, the planning and control data may instruct vehicle 300 to move 10 meters at a speed of 30 miles per hour (mph), then change to a right lane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls and drives the autonomous vehicle, by sending proper commands or signals to vehicle control system 111, according to a route or path defined by the planning and control data. The planning and control data include sufficient information to drive the vehicle from a first point to a second point of a route or path using appropriate vehicle settings or driving parameters (e.g., throttle, braking, steering commands) at different points in time along the path or route.

In one embodiment, the planning phase is performed in a number of planning cycles, also referred to as driving cycles, such as, for example, in every time interval of 100 milliseconds (ms). For each of the planning cycles or driving cycles, one or more control commands will be issued based on the planning and control data. That is, for every 100 ms, planning module 305 plans a next route segment or path segment, for example, including a target position and the time required for the ADV to reach the target position. Alternatively, planning module 305 may further specify the specific speed, direction, and/or steering angle, etc. In one embodiment, planning module 305 plans a route segment or path segment for the next predetermined period of time such as 5 seconds. For each planning cycle, planning module 305 plans a target position for the current cycle (e.g., next 5 seconds) based on a target position planned in a previous cycle. Control module 306 then generates one or more control commands (e.g., throttle, brake, steering control commands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integrated as an integrated module. Decision module 304/planning module 305 may include a navigation system or functionalities of a navigation system to determine a driving path for the autonomous vehicle. For example, the navigation system may determine a series of speeds and directional headings to affect movement of the autonomous vehicle along a path that substantially avoids perceived obstacles while generally advancing the autonomous vehicle along a roadway-based path leading to an ultimate destination. The destination may be set according to user inputs via user interface system 113. The navigation system may update the driving path dynamically while the autonomous vehicle is in operation. The navigation system can incorporate data from a GPS system and one or more maps so as to determine the driving path for the autonomous vehicle.

An obstacle filtering module 403 filters one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates, as will be described in further detail below. Module 403 may be integrated with another one of modules 301-307, such as, for example, perception module 302.

Referring to FIG. 4, a block diagram 400 illustrating various modules usable according to one embodiment of the disclosure is shown. A point cloud 401 comprising a plurality of points is generated based on outputs of the LIDAR device/unit 215. The point cloud 401 may represent the 3D shapes of features in the surrounding environment. Each point may have its own set of X, Y and Z coordinates, and may be associated with an intensity measurement. The intensity measurement indicates the return strength of the laser pulse that generated the point. One or more obstacle candidates 402 are determined based on the point cloud at the perception module 302. The one or more obstacle candidates 402 are filtered at an obstacle filtering module 403 to remove a first set of obstacle candidates in the one or more obstacle candidates 402 that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates 402. The noise may be caused by dusts or other particles in the surrounding environment. In one embodiment, the first set of obstacle candidates that correspond to noise comprise obstacle candidates that correspond to points corresponding to dusts. In other words, false positive obstacles may be generated at the perception module 302 due to the noise, and these false positive obstacles should be removed at the obstacle filtering module 403.

One or more recognized obstacles 404 comprising the obstacle candidates that have not been removed at the obstacle filtering module 403 are determined. Operations of an autonomous vehicle are controlled by the control module 306 based on the recognized obstacles 404.

In one embodiment, the characteristics associated with points that correspond to each of the one or more obstacle candidates based on which the one or more obstacle candidates are filtered comprise one or more of: an intensity measurement distribution of the points that correspond to each of the one or more obstacle candidates, a spatial distribution of the points that correspond to each of the one or more obstacle candidates, or a combination thereof.

In one embodiment, filtering the one or more obstacle candidates comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low intensity measurements to a total quantity of points corresponding to the obstacle candidate is above a first threshold. In one embodiment, the first threshold may be approximately 0.9. A point is associated with a low intensity measurement when the intensity measurement of the point is below an intensity threshold. It should be appreciated that in other functionally equivalent embodiments, other ratios that capture the same numerical relationship may be used instead, with the first threshold, possibly together with the inequality relationship, adjusted accordingly. For example, a ratio of a quantity of points associated with low intensity measurements to a quantity of points not associated with low intensity measurements (e.g., point associated with high intensity measurements) may be utilized instead, with the first threshold adjusted accordingly. The actual ratio, inequality relationship, or first threshold used does not limit the disclosure.

Referring to FIGS. 5A and 5B, diagrams 500A, 500B illustrating a spatial distribution of LIDAR points that correspond to an obstacle according to one embodiment are shown. The LIDAR unit 215 emits rotating laser pulses 502 to scan the environment. Based on pulses reflected off of an obstacle 501, a plurality of points 401 that correspond to the obstacle 501 may be determined. For a real obstacle, such as the obstacle 501, the corresponding points 401 should collectively have a shape that approximately resemble the capital letter L (or rotated or flipped versions of the capital letter L). This observation may be utilized to keep real obstacles and remove false positive obstacle candidates that correspond to points that have been generated due to the environmental noise such as dusts or other particles, as will be further described in detail below. It should be further appreciated that for a real obstacle, the change of distribution of corresponding points at different heights is relatively modest, whereas the change of distribution of points at different heights can be significant for a group of points that have been generated due to the environmental noise and correspond to a false positive obstacle candidate. This observation may also be utilized to remove false positive obstacle candidates, as will be further described in detail below. FIG. 5B illustrates that projection 503A, 503B of the points 401 to two orthogonal dimensions (e.g., a length side and a width side) of a horizontal plane, and the determination of respective standard deviations (σ) of the projected points along the two dimensions. The standard deviations may be utilized to remove false positive obstacle candidates, as will be further described in detail below.

Referring back to FIG. 4, at the obstacle filtering module 403, in one embodiment, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold, points corresponding to the obstacle candidate are projected to a first and a second dimensions of a horizontal plane. The first dimension and the second dimension may be, e.g., a length side and a width side, respectively, and are orthogonal to each other. A first standard deviation of the projected points along the first dimension and a second standard deviation of the projected points along the second dimension are determined. The obstacle candidate is removed when the first standard deviation is above a first standard deviation threshold, or when the second standard deviation is above a second standard deviation threshold. In one embodiment, the obstacle candidate is removed when the first standard deviation is above the first standard deviation threshold and the second standard deviation is above the second standard deviation threshold.

In one embodiment, the first standard deviation threshold is equal to the second standard deviation threshold. In another embodiment, the first standard deviation threshold and the second standard deviation threshold are different. In one embodiment, either of the first standard deviation threshold and the second standard deviation threshold may be approximately 0.6. In still further functionally equivalent embodiments, other measurements of dispersion than the standard deviation may be utilized instead.

In one embodiment, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold, points corresponding to the obstacle candidate are projected to an area of the horizontal plane. The area of the horizontal plane is associated with a grid comprising a first quantity of squares. Within the first quantity of squares, a second quantity of squares each of which contains at least one projected point are determined. The obstacle candidate is removed when a ratio of the second quantity to the first quantity is above a second threshold. In one embodiment, the second threshold may be approximately 0.6. It should be appreciated that in other functionally equivalent embodiments, other ratios that capture the same numerical relationship may be used instead, with the second threshold, possibly together with the inequality relationship, adjusted accordingly. For example, a ratio of a quantity of squares that contain at least one projected point (i.e., the second quantity) to a quantity of squares that contain no projected points (i.e., the first quantity-the second quantity) may be utilized instead, with the second threshold adjusted accordingly. The actual ratio, inequality relationship, or second threshold used does not limit the disclosure.

In one embodiment, filtering the one or more obstacle candidates further comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low heights to a total quantity of points corresponding to the obstacle candidate is below a third threshold. In one embodiment, the third threshold may be approximately 0.5. The height of a point may correspond to a distance between the point and the ground level or another predetermined reference level. A point is associated with a low height when the height of the point is below a height threshold. It should be appreciated that in other functionally equivalent embodiments, other ratios that capture the same numerical relationship may be used instead, with the third threshold, possibly together with the inequality relationship, adjusted accordingly. For example, a ratio of a quantity of points associated with low heights to a quantity of points not associated with low heights (e.g., point associated with high heights) may be utilized instead, with the third threshold adjusted accordingly. The actual ratio, inequality relationship, or third threshold used does not limit the disclosure.

In one embodiment, filtering the one or more obstacle candidates further comprises removing obstacle candidates each of which is absent in perception results for any of a predetermined number of immediately previous perception cycles (which may also be referred to as frames). The number of immediately previous perception cycles utilized is a predetermined hyper-parameter, and may be determined empirically.

Various thresholds have been described hereinafter. It should be appreciated that in different embodiments, different values may be utilized for the various thresholds, and the actual threshold values do not limit the disclosure.

Referring to FIG. 6, a flowchart illustrating an example method 600 for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle according to one embodiment is shown. The process illustrated in FIG. 6 may be implemented in hardware, software, or a combination thereof. At block 610, a point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. At block 620, one or more obstacle candidates are determined based on the point cloud. At block 630, the one or more obstacle candidates are filtered to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates. At block 640, one or more recognized obstacles comprising the obstacle candidates that have not been removed are determined. At block 650, operations of an autonomous vehicle are controlled based on the recognized obstacles.

Referring to FIG. 7, a flowchart illustrating an example method 700 for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle according to one embodiment is shown. The process illustrated in FIG. 7 may be implemented in hardware, software, or a combination thereof. The method 700 may be performed at block 630 of FIG. 6. At block 710, obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low intensity measurements to a total quantity of points corresponding to the obstacle candidate is above a first threshold are removed. A point is associated with a low intensity measurement when the intensity measurement of the point is below an intensity threshold. For each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold, at block 720, points corresponding to the obstacle candidate are projected to a first and a second dimensions of a horizontal plane. The first dimension and the second dimension being orthogonal to each other. At block 730, a first standard deviation of the projected points along the first dimension and a second standard deviation of the projected points along the second dimension are determined. At block 740, the obstacle candidate is removed when the first standard deviation is above a first standard deviation threshold, or when the second standard deviation is above a second standard deviation threshold.

Therefore, according to embodiments of the disclosure, false positive obstacle candidates that correspond to LIDAR points that in turn correspond to noise, such as the noise caused by dusts or other particles, may be removed. The removal of the false positive obstacle candidates reduces the likelihood that the operations of the autonomous vehicle may be improperly interfered with by the false positive obstacle candidates.

Note that some or all of the components as shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components can be implemented as software installed and stored in a persistent storage device, which can be loaded and executed in a memory by a processor (not shown) to carry out the processes or operations described throughout this application. Alternatively, such components can be implemented as executable code programmed or embedded into dedicated hardware such as an integrated circuit (e.g., an application specific IC or ASIC), a digital signal processor (DSP), or a field programmable gate array (FPGA), which can be accessed via a corresponding driver and/or operating system from an application. Furthermore, such components can be implemented as specific hardware logic in a processor or processor core as part of an instruction set accessible by a software component via one or more specific instructions.

Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices).

The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the disclosure as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense. 

1. A computer-implemented method, comprising: generating a point cloud comprising a plurality of points based on outputs of a light detection and range (LIDAR) device; determining one or more obstacle candidates based on the point cloud; filtering the one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates; determining one or more recognized obstacles comprising the obstacle candidates that have not been removed; and controlling operations of an autonomous vehicle based on the recognized obstacles.
 2. The method of claim 1, wherein the first set of obstacle candidates that correspond to noise comprise obstacle candidates that correspond to points corresponding to dusts.
 3. The method of claim 1, wherein the characteristics associated with points that correspond to each of the one or more obstacle candidates based on which the one or more obstacle candidates are filtered comprise one or more of: an intensity measurement distribution of the points that correspond to each of the one or more obstacle candidates, a spatial distribution of the points that correspond to each of the one or more obstacle candidates, or a combination thereof.
 4. The method of claim 3, wherein filtering the one or more obstacle candidates comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low intensity measurements to a total quantity of points corresponding to the obstacle candidate is above a first threshold, wherein a point is associated with a low intensity measurement when the intensity measurement of the point is below an intensity threshold.
 5. The method of claim 4, wherein filtering the one or more obstacle candidates further comprises, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold: projecting points corresponding to the obstacle candidate to a first and a second dimensions of a horizontal plane, the first dimension and the second dimension being orthogonal to each other; determining a first standard deviation of the projected points along the first dimension and a second standard deviation of the projected points along the second dimension; and removing the obstacle candidate when the first standard deviation is above a first standard deviation threshold, or when the second standard deviation is above a second standard deviation threshold.
 6. The method of claim 5, wherein the obstacle candidate is removed when the first standard deviation is above the first standard deviation threshold and the second standard deviation is above the second standard deviation threshold.
 7. The method of claim 5, wherein the first standard deviation threshold is equal to the second standard deviation threshold.
 8. The method of claim 5, wherein the first standard deviation threshold and the second standard deviation threshold are different.
 9. The method of claim 5, wherein filtering the one or more obstacle candidates further comprises, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold: projecting points corresponding to the obstacle candidate to an area of the horizontal plane, the area of the horizontal plane being associated with a grid comprising a first quantity of squares; determining, within the first quantity of squares, a second quantity of squares each of which contains at least one projected point; and removing the obstacle candidate when a ratio of the second quantity to the first quantity is above a second threshold.
 10. The method of claim 9, wherein filtering the one or more obstacle candidates further comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low heights to a total quantity of points corresponding to the obstacle candidate is below a third threshold, wherein a point is associated with a low height when the height of the point is below a height threshold.
 11. The method of claim 10, wherein filtering the one or more obstacle candidates further comprises removing obstacle candidates each of which is absent in perception results for any of a predetermined number of immediately previous perception cycles.
 12. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations, the operations comprising: generating a point cloud comprising a plurality of points based on outputs of a light detection and range (LIDAR) device; determining one or more obstacle candidates based on the point cloud; filtering the one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates; determining one or more recognized obstacles comprising the obstacle candidates that have not been removed; and controlling operations of an autonomous vehicle based on the recognized obstacles.
 13. The non-transitory machine-readable medium of claim 12, wherein the first set of obstacle candidates that correspond to noise comprise obstacle candidates that correspond to points corresponding to dusts.
 14. The non-transitory machine-readable medium of claim 12, wherein the characteristics associated with points that correspond to each of the one or more obstacle candidates based on which the one or more obstacle candidates are filtered comprise one or more of: an intensity measurement distribution of the points that correspond to each of the one or more obstacle candidates, a spatial distribution of the points that correspond to each of the one or more obstacle candidates, or a combination thereof.
 15. The non-transitory machine-readable medium of claim 14, wherein filtering the one or more obstacle candidates comprises removing obstacle candidates each of which corresponds to points in which a ratio of a quantity of points associated with low intensity measurements to a total quantity of points corresponding to the obstacle candidate is above a first threshold, wherein a point is associated with a low intensity measurement when the intensity measurement of the point is below an intensity threshold.
 16. The non-transitory machine-readable medium of claim 15, wherein filtering the one or more obstacle candidates further comprises, for each remaining obstacle candidate whose recognized obstacle type is vehicle or whose recognized physical size is above a size threshold: projecting points corresponding to the obstacle candidate to a first and a second dimensions of a horizontal plane, the first dimension and the second dimension being orthogonal to each other; determining a first standard deviation of the projected points along the first dimension and a second standard deviation of the projected points along the second dimension; and removing the obstacle candidate when the first standard deviation is above a first standard deviation threshold, or when the second standard deviation is above a second standard deviation threshold.
 17. The non-transitory machine-readable medium of claim 16, wherein the obstacle candidate is removed when the first standard deviation is above the first standard deviation threshold and the second standard deviation is above the second standard deviation threshold.
 18. The non-transitory machine-readable medium of claim 16, wherein the first standard deviation threshold is equal to the second standard deviation threshold.
 19. The non-transitory machine-readable medium of claim 16, wherein the first standard deviation threshold and the second standard deviation threshold are different.
 20. (canceled)
 21. (canceled)
 22. (canceled)
 23. A data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations, the operations including: generating a point cloud comprising a plurality of points based on outputs of a light detection and range (LIDAR) device; determining one or more obstacle candidates based on the point cloud; filtering the one or more obstacle candidates to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates; determining one or more recognized obstacles comprising the obstacle candidates that have not been removed; and controlling operations of an autonomous vehicle based on the recognized obstacles. 24-33. (canceled) 